from sklearn_benchmarks.reporting.hp_match import HPMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HPMatchReporting(against_lib="sklearnex", config="config.yml")
reporting.make_report()

We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=brute.

WARNING! Mismatch between validation scores for 14 predictions (58.33%). See below.

	estimator	function	n_samples_train	n_samples	n_features	accuracy_score_sklearn	accuracy_score_sklearnex	diff_accuracy_scores
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.663	0.687	0.024
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.757	0.742	0.015
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.882	0.875	0.007
8	KNeighborsClassifier_brute_force	predict	100000	1	100	1.000	0.000	1.000
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.882	0.875	0.007
11	KNeighborsClassifier_brute_force	predict	100000	1	100	1.000	0.000	1.000
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.757	0.742	0.015
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.663	0.687	0.024
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.896	0.967	0.071
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.922	0.974	0.052
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.929	0.975	0.046
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.929	0.975	0.046
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.922	0.974	0.052
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.896	0.967	0.071

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	7.144	0.0	-1	1	0.045	0.001	0.247	0.247	See	See
3	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	7.103	0.0	-1	5	0.046	0.000	0.246	0.246	See	See
6	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	7.121	0.0	1	100	0.046	0.000	0.244	0.244	See	See
9	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	7.080	0.0	-1	100	0.046	0.000	0.245	0.245	See	See
12	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	7.218	0.0	1	5	0.047	0.000	0.238	0.238	See	See
15	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	7.196	0.0	1	1	0.047	0.000	0.238	0.238	See	See
18	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	0.381	0.0	-1	1	0.008	0.000	0.519	0.519	See	See
21	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	0.380	0.0	-1	5	0.008	0.000	0.511	0.511	See	See
24	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	0.374	0.0	1	100	0.008	0.000	0.522	0.522	See	See
27	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	0.377	0.0	-1	100	0.008	0.000	0.515	0.515	See	See
30	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	0.376	0.0	1	5	0.008	0.000	0.519	0.519	See	See
33	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	0.377	0.0	1	1	0.008	0.000	0.519	0.519	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.033	0.165	0.000	0.002	-1	1	0.178	0.003	11.432	11.433	See	See
2	KNeighborsClassifier_brute_force	predict	100000	1	100	0.024	0.004	0.000	0.024	-1	1	0.008	0.000	2.856	2.856	See	See
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.877	0.023	0.000	0.003	-1	5	0.182	0.006	15.769	15.778	See	See
5	KNeighborsClassifier_brute_force	predict	100000	1	100	0.023	0.002	0.000	0.023	-1	5	0.009	0.000	2.744	2.745	See	See
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.047	0.004	0.000	0.002	1	100	0.217	0.009	9.417	9.425	See	See
8	KNeighborsClassifier_brute_force	predict	100000	1	100	0.020	0.000	0.000	0.020	1	100	0.009	0.000	2.269	2.269	See	See
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.857	0.019	0.000	0.003	-1	100	0.215	0.002	13.259	13.259	See	See
11	KNeighborsClassifier_brute_force	predict	100000	1	100	0.024	0.002	0.000	0.024	-1	100	0.009	0.001	2.651	2.662	See	See
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.017	0.003	0.000	0.002	1	5	0.181	0.003	11.140	11.141	See	See
14	KNeighborsClassifier_brute_force	predict	100000	1	100	0.020	0.000	0.000	0.020	1	5	0.008	0.000	2.303	2.304	See	See
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.152	0.002	0.001	0.001	1	1	0.177	0.001	6.495	6.495	See	See
17	KNeighborsClassifier_brute_force	predict	100000	1	100	0.019	0.000	0.000	0.019	1	1	0.008	0.000	2.228	2.229	See	See
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.816	0.032	0.000	0.002	-1	1	0.026	0.000	70.635	70.639	See	See
20	KNeighborsClassifier_brute_force	predict	100000	1	2	0.006	0.005	0.000	0.006	-1	1	0.001	0.000	10.135	10.166	See	See
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.721	0.030	0.000	0.003	-1	5	0.027	0.000	102.081	102.082	See	See
23	KNeighborsClassifier_brute_force	predict	100000	1	2	0.010	0.005	0.000	0.010	-1	5	0.001	0.000	14.850	14.904	See	See
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.959	0.002	0.000	0.002	1	100	0.062	0.002	31.566	31.579	See	See
26	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.000	0.003	1	100	0.001	0.000	3.966	3.996	See	See
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.715	0.031	0.000	0.003	-1	100	0.060	0.001	45.205	45.208	See	See
29	KNeighborsClassifier_brute_force	predict	100000	1	2	0.007	0.004	0.000	0.007	-1	100	0.001	0.000	9.273	9.328	See	See
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.936	0.003	0.000	0.002	1	5	0.027	0.001	70.565	70.623	See	See
32	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.000	0.003	1	5	0.001	0.000	4.569	4.596	See	See
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.066	0.004	0.000	0.001	1	1	0.026	0.000	41.164	41.165	See	See
35	KNeighborsClassifier_brute_force	predict	100000	1	2	0.002	0.000	0.000	0.002	1	1	0.001	0.000	2.832	2.842	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=kd_tree.

WARNING! Mismatch between validation scores for 12 predictions (50.0%). See below.

	estimator	function	n_samples_train	n_samples	n_features	accuracy_score_sklearn	accuracy_score_sklearnex	diff_accuracy_scores
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.929	0.910	0.019
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.946	0.941	0.005
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.951	0.940	0.011
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.951	0.940	0.011
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.946	0.941	0.005
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.929	0.910	0.019
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.891	0.879	0.012
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.911	0.905	0.006
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.894	0.917	0.023
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.894	0.917	0.023
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.911	0.905	0.006
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.891	0.879	0.012

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2.921	0.092	0.027	0.0	-1	1	0.753	0.021	3.881	3.883	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.562	0.032	0.022	0.0	-1	5	0.745	0.014	4.780	4.781	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.640	0.059	0.022	0.0	1	100	0.731	0.009	4.982	4.982	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.566	0.052	0.022	0.0	-1	100	0.762	0.007	4.679	4.679	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.516	0.080	0.023	0.0	1	5	0.741	0.011	4.745	4.746	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.571	0.037	0.022	0.0	1	1	0.774	0.016	4.615	4.616	See	See
18	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.001	0.023	0.0	-1	1	0.003	0.002	0.232	0.273	See	See
21	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.029	0.0	-1	5	0.002	0.001	0.299	0.364	See	See
24	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.031	0.0	1	100	0.001	0.000	0.575	0.580	See	See
27	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.031	0.0	-1	100	0.001	0.000	0.555	0.577	See	See
30	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.031	0.0	1	5	0.001	0.000	0.631	0.632	See	See
33	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.031	0.0	1	1	0.001	0.000	0.649	0.649	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.827	0.991	0.000	0.001	-1	1	0.114	0.003	7.258	7.260	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.000	0.000	0.003	-1	1	0.000	0.000	9.657	10.121	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.027	0.278	0.000	0.001	-1	5	0.207	0.004	4.963	4.964	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	-1	5	0.000	0.000	7.650	8.127	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	5.487	0.359	0.000	0.005	1	100	0.633	0.011	8.665	8.666	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	1	100	0.001	0.000	3.820	4.049	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	3.044	0.124	0.000	0.003	-1	100	0.639	0.009	4.760	4.760	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.005	0.001	0.000	0.005	-1	100	0.001	0.000	6.343	6.725	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.666	0.236	0.000	0.002	1	5	0.215	0.003	7.749	7.750	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.002	0.000	0.000	0.002	1	5	0.000	0.000	3.593	3.857	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.893	0.181	0.000	0.001	1	1	0.114	0.002	7.862	7.863	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.000	0.000	0.001	1	1	0.000	0.000	4.150	4.464	See	See
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.025	0.014	0.001	0.000	-1	1	0.000	0.000	61.247	61.694	See	See
20	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	1	0.000	0.000	21.645	22.452	See	See
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.022	0.001	0.001	0.000	-1	5	0.001	0.000	34.834	34.852	See	See
23	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	5	0.000	0.000	21.445	22.223	See	See
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.033	0.001	0.000	0.000	1	100	0.004	0.000	7.417	7.417	See	See
26	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	100	0.000	0.000	5.818	6.163	See	See
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.035	0.003	0.000	0.000	-1	100	0.004	0.000	7.805	7.805	See	See
29	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	100	0.000	0.000	20.015	21.025	See	See
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.020	0.000	0.001	0.000	1	5	0.001	0.000	31.241	31.260	See	See
32	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	5	0.000	0.000	6.261	6.576	See	See
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.019	0.000	0.001	0.000	1	1	0.000	0.000	48.849	48.919	See	See
35	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	1	0.000	0.000	6.664	7.074	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	0.510	0.056	30	0.031	0.0	random	0.376	0.027	1.356	1.360	See	See
3	KMeans_tall	fit	1000000	1000000	2	0.563	0.018	30	0.028	0.0	k-means++	0.409	0.025	1.374	1.377	See	See
6	KMeans_tall	fit	1000000	1000000	100	5.597	0.213	30	0.143	0.0	random	2.678	0.064	2.090	2.090	See	See
9	KMeans_tall	fit	1000000	1000000	100	5.758	0.026	30	0.139	0.0	k-means++	2.814	0.009	2.046	2.046	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	0.002	0.000	30	0.010	0.000	random	0.0	0.0	9.659	13.489	See	See
2	KMeans_tall	predict	1000000	1	2	0.001	0.000	30	0.000	0.001	random	0.0	0.0	8.803	13.997	See	See
4	KMeans_tall	predict	1000000	1000	2	0.002	0.001	30	0.010	0.000	k-means++	0.0	0.0	12.743	14.598	See	See
5	KMeans_tall	predict	1000000	1	2	0.002	0.000	30	0.000	0.002	k-means++	0.0	0.0	17.585	18.367	See	See
7	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.482	0.000	random	0.0	0.0	6.480	7.040	See	See
8	KMeans_tall	predict	1000000	1	100	0.001	0.000	30	0.001	0.001	random	0.0	0.0	13.819	14.219	See	See
10	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.495	0.000	k-means++	0.0	0.0	6.244	6.748	See	See
11	KMeans_tall	predict	1000000	1	100	0.001	0.000	30	0.001	0.001	k-means++	0.0	0.0	13.204	13.586	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

WARNING! Mismatch between validation scores for 4 predictions (50.0%). See below.

	estimator	function	n_samples_train	n_samples	n_features	adjusted_rand_score_sklearn	adjusted_rand_score_sklearnex	diff_adjusted_rand_scores
1	KMeans_short	predict	10000	1000	2	0.000126	-0.000965	0.001090
4	KMeans_short	predict	10000	1000	2	0.001245	-0.000750	0.001995
7	KMeans_short	predict	10000	1000	100	0.278733	0.293767	0.015034
10	KMeans_short	predict	10000	1000	100	0.317011	0.256968	0.060044

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	0.072	0.000	20	0.002	0.0	random	0.026	0.001	2.762	2.765	See	See
3	KMeans_short	fit	10000	10000	2	0.210	0.002	20	0.001	0.0	k-means++	0.080	0.000	2.609	2.609	See	See
6	KMeans_short	fit	10000	10000	100	0.195	0.005	20	0.041	0.0	random	0.108	0.003	1.809	1.809	See	See
9	KMeans_short	fit	10000	10000	100	0.571	0.035	20	0.014	0.0	k-means++	0.317	0.003	1.805	1.805	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.009	0.000	random	0.000	0.0	3.989	4.002	See	See
2	KMeans_short	predict	10000	1	2	0.001	0.0	20	0.000	0.001	random	0.000	0.0	14.104	14.443	See	See
4	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.009	0.000	k-means++	0.000	0.0	3.851	3.862	See	See
5	KMeans_short	predict	10000	1	2	0.001	0.0	20	0.000	0.001	k-means++	0.000	0.0	13.539	13.920	See	See
7	KMeans_short	predict	10000	1000	100	0.003	0.0	20	0.314	0.000	random	0.001	0.0	2.698	2.708	See	See
8	KMeans_short	predict	10000	1	100	0.001	0.0	20	0.001	0.001	random	0.000	0.0	10.339	10.559	See	See
10	KMeans_short	predict	10000	1000	100	0.002	0.0	20	0.330	0.000	k-means++	0.001	0.0	2.580	2.588	See	See
11	KMeans_short	predict	10000	1	100	0.001	0.0	20	0.001	0.001	k-means++	0.000	0.0	10.181	10.298	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

WARNING! Mismatch between validation scores for 2 predictions (50.0%). See below.

	estimator	function	n_samples_train	n_samples	n_features	accuracy_score_sklearn	accuracy_score_sklearnex	diff_accuracy_scores
1	LogisticRegression	predict	1000000	1000	100	0.56	0.55	0.01
4	LogisticRegression	predict	1000	100	10000	0.35	0.28	0.07

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	11.156	0.512	[20]	0.072	0.000	1.927	0.009	5.790	5.790	See	See
3	LogisticRegression	fit	1000	1000	10000	0.934	0.500	[26]	0.086	0.001	0.954	0.033	0.979	0.979	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	0.000	0.0	[20]	2.520	0.0	0.001	0.001	0.475	0.911	See	See
2	LogisticRegression	predict	1000000	1	100	0.000	0.0	[20]	0.017	0.0	0.000	0.000	0.344	0.350	See	See
4	LogisticRegression	predict	1000	100	10000	0.002	0.0	[26]	5.037	0.0	0.005	0.001	0.309	0.313	See	See
5	LogisticRegression	predict	1000	1	10000	0.000	0.0	[26]	1.094	0.0	0.002	0.000	0.047	0.047	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

WARNING! Mismatch between validation scores for 1 prediction (25.0%). See below.

	estimator	function	n_samples_train	n_samples	n_features	r2_score_sklearn	r2_score_sklearnex	diff_r2_scores
1	Ridge	predict	1000	1000	10000	0.082567	0.122191	0.039624

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	0.175	0.002	0.457	0.0	0.183	0.002	0.957	0.957	See	See
3	Ridge	fit	1000000	1000000	100	1.105	0.045	0.724	0.0	0.308	0.257	3.592	4.680	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	0.011	0.0	6.997	0.0	0.019	0.001	0.612	0.613	See	See
2	Ridge	predict	1000	1	10000	0.000	0.0	1.371	0.0	0.000	0.000	0.587	0.632	See	See
4	Ridge	predict	1000000	1000	100	0.000	0.0	5.490	0.0	0.000	0.000	0.506	0.744	See	See
5	Ridge	predict	1000000	1	100	0.000	0.0	0.016	0.0	0.000	0.000	0.634	0.668	See	See